Instructions to use laion/Qwen3-32B-SweSmith-20step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use laion/Qwen3-32B-SweSmith-20step with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="laion/Qwen3-32B-SweSmith-20step")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("laion/Qwen3-32B-SweSmith-20step")
model = AutoModelForCausalLM.from_pretrained("laion/Qwen3-32B-SweSmith-20step")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use laion/Qwen3-32B-SweSmith-20step with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "laion/Qwen3-32B-SweSmith-20step"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "laion/Qwen3-32B-SweSmith-20step",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/laion/Qwen3-32B-SweSmith-20step

SGLang

How to use laion/Qwen3-32B-SweSmith-20step with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "laion/Qwen3-32B-SweSmith-20step" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "laion/Qwen3-32B-SweSmith-20step",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "laion/Qwen3-32B-SweSmith-20step" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "laion/Qwen3-32B-SweSmith-20step",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use laion/Qwen3-32B-SweSmith-20step with Docker Model Runner:
```
docker model run hf.co/laion/Qwen3-32B-SweSmith-20step
```

Qwen3-32B-SweSmith-20step

RL-trained Qwen3-32B on SWEsmith.

Training Details

Base model: Qwen/Qwen3-32B
Training method: rloo_n
Training data: 2,500 SWEsmith tasks
Steps: 20 global steps
Infrastructure: 16x4 GH200 GPU nodes (GCP), FSDP2 with TP=2 for inference engines (24 inference engines + 4 policy/ref nodes)
Sandbox environment: Beta9/Beam containers for code execution

Training Curve

Metric	Step 1	Step 10	Step 20
Avg Raw Reward	0.031	0.041	0.072
Pass@8	0.141	0.156	0.234

Downloads last month: 6

Safetensors

Model size

33B params

Tensor type

BF16

Model tree for laion/Qwen3-32B-SweSmith-20step

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Qwen/Qwen3-32B

Finetuned

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